Emotion Detection Research Articles

DEEP LEARNING BASED EMOTION DETECTION SYSTEM

Emotion detection plays a crucial role in human-computer interaction, affective computing, and psychological research. Traditional methods of emotion detection often rely on manual annotations or subjective assessments, which can be time- consuming and prone to biases. In this paper, we propose a Deep Learning-Based Emotion Detection System that leverages advanced neural network architectures to automatically recognize emotions from facial expressions in real-time. The system utilizes convolutional neural networks (CNNs) for feature extraction from facial images and recurrent neural networks (RNNs) for sequence modeling of temporal dynamics in facial expressions. Our proposed system addresses key challenges in emotion detection, including variations in facial expressions, lighting conditions, and occlusions. By training on large-scale datasets of labeled facial images, the system learns to accurately classify emotions into predefined categories such as happiness, sadness, anger, fear, disgust, and surprise. We evaluate the performance of the system using standard metrics such as accuracy, precision, recall, and F1-score, demonstrating its effectiveness in real- world scenarios. The results indicate that the Deep Learning-Based Emotion Detection System achieves state-of-the-art performance in recognizing emotions from facial expressions, outperforming traditional methods. The system's ability to analyze facial dynamics in real-time opens up opportunities for applications in human-computer interaction, virtual reality, mental health monitoring, and personalized user experiences. Keywords: Deep Learning, Emotion Detection, Facial Expression Recognition, Convolutional Neural Networks, Recurrent Neural Networks, Affective Computing. Keywords: emotion Detection, Machine Learning, Classifier, Natural Language Processing

A review on emotion detection by using deep learning techniques

Along with the growth of Internet with its numerous potential applications and diverse fields, artificial intelligence (AI) and sentiment analysis (SA) have become significant and popular research areas. Additionally, it was a key technology that contributed to the Fourth Industrial Revolution (IR 4.0). The subset of AI known as emotion recognition systems facilitates communication between IR 4.0 and IR 5.0. Nowadays users of social media, digital marketing, and e-commerce sites are increasing day by day resulting in massive amounts of unstructured data. Medical, marketing, public safety, education, human resources, business, and other industries also use the emotion recognition system widely. Hence it provides a large amount of textual data to extract the emotions from them. The paper presents a systematic literature review of the existing literature published between 2013 to 2023 in text-based emotion detection. This review scrupulously summarized 330 research papers from different conferences, journals, workshops, and dissertations. This paper explores different approaches, methods, different deep learning models, key aspects, description of datasets, evaluation techniques, Future prospects of deep learning, challenges in existing studies and presents limitations and practical implications.

Assessing Feature Importance in Eye-Tracking Data within Virtual Reality Using Explainable Artificial Intelligence Techniques

Our research systematically investigates the cognitive and emotional processes revealed through eye movements within the context of virtual reality (VR) environments. We assess the utility of eye-tracking data for predicting emotional states in VR, employing explainable artificial intelligence (XAI) to advance the interpretability and transparency of our findings. Utilizing the VR Eyes: Emotions dataset (VREED) alongside an extra trees classifier enhanced by SHapley Additive ExPlanations (SHAP) and local interpretable model agnostic explanations (LIME), we rigorously evaluate the importance of various eye-tracking metrics. Our results identify significant correlations between metrics such as saccades, micro-saccades, blinks, and fixations and specific emotional states. The application of SHAP and LIME elucidates these relationships, providing deeper insights into the emotional responses triggered by VR. These findings suggest that variations in eye feature patterns serve as indicators of heightened emotional arousal. Not only do these insights advance our understanding of affective computing within VR, but they also highlight the potential for developing more responsive VR systems capable of adapting to user emotions in real-time. This research contributes significantly to the fields of human-computer interaction and psychological research, showcasing how XAI can bridge the gap between complex machine-learning models and practical applications, thereby facilitating the creation of reliable, user-sensitive VR experiences. Future research may explore the integration of multiple physiological signals to enhance emotion detection and interactive dynamics in VR.

Geo-Phenomenology: A Qualitative and Humanistic GIS Approach to Exploring Lived Experience

Critical discussions are developing to combine geographic information systems (GIS) and qualitative methods as an alternative approach to traditional quantitative GIS, but the possibility of integrating spatial information technology and phenomenology is still unknown. This study explores a new qualitative and humanistic GIS approach called geo-phenomenology. It attempts to improve the ability to validate and authenticate findings and to gain a holistic understanding of the complex interplay among space, place, and human emotion through a combination of phenomenology, natural language processing methods, and GIS. To illustrate this approach, this study focuses on international students and examines the effects of geographic mobility on individuals’ sense of attachment and belonging. Importantly, it allows for a more sensitive approach to the spatial scale of material culture as a mediator of sense of place. The overlay of emotional mapping and land use provided a perspective for narrative interpretation and hypothesis building, ultimately leading to the discovery of key evidence determining the main themes of international students’ sense of place attachment and belonging. By combining quantitative emotion detection with qualitative phenomenological interpretation in GIS, this study concludes that geo-phenomenology can help us consider the rich nuances of lifeworlds from multiple perspectives.

Emotion AWARE: an artificial intelligence framework for adaptable, robust, explainable, and multi-granular emotion analysis

Emotions are fundamental to human behaviour. How we feel, individually and collectively, determines how humanity evolves and advances into our shared future. The rapid digitalisation of our personal, social and professional lives means we are frequently using digital media to express, understand and respond to emotions. Although recent developments in Artificial Intelligence (AI) are able to analyse sentiment and detect emotions, they are not effective at comprehending the complexity and ambiguity of digital emotion expressions in knowledge-focused activities of customers, people, and organizations. In this paper, we address this challenge by proposing a novel AI framework for the adaptable, robust, and explainable detection of multi-granular assembles of emotions. This framework consolidates lexicon generation and finetuned Large Language Model (LLM) approaches to formulate multi-granular assembles of two, eight and fourteen emotions. The framework is robust to ambiguous emotion expressions that are implied in conversation, adaptable to domain-specific emotion semantics, and the assembles are explainable using constituent terms and intensity. We conducted nine empirical studies using datasets representing diverse human emotion behaviours. The results of these studies comprehensively demonstrate and evaluate the core capabilities of the framework, and consistently outperforms state-of-the-art approaches in adaptable, robust, and explainable multi-granular emotion detection.

An Effective Data Fusion Methodology for Multi-modal Emotion Recognition: A Survey

Emotion recognition is a pivotal area of research with applications spanning education, healthcare, and intelligent customer service. Multimodal emotion recognition (MER) has emerged as a superior approach by integrating multiple modalities such as speech, text, and facial expressions, offering enhanced accuracy and robustness over unimodal methods. This paper reviews the evolution and current state of MER, highlighting its significance, challenges, and methodologies. We delve into various datasets, including IEMOCAP and MELD, providing a comparative analysis of their strengths and limitations. The literature review covers recent advancements in deep learning techniques, focusing on fusion strategies like early, late, and hybrid fusion. Identified gaps include issues related to data redundancy, feature extraction complexity, and real-time detection. Our proposed methodology leverages deep learning for feature extraction and a hybrid fusion approach to improve emotion detection accuracy. This research aims to guide future studies in addressing current limitations and advancing the field of MER. The main of this paper review recent methodologies in multimodal emotion recognition, analyze different data fusion techniques,identify challenges and research gaps.

Transfer learning for emotion detection in conversational text: a hybrid deep learning approach with pre-trained embeddings

Transfer learning for emotion detection in conversational text: a hybrid deep learning approach with pre-trained embeddings

Motor-sensory biases are associated with cognitive and social abilities in humans

Across vertebrates, adaptive behaviors, like feeding and avoiding predators, are linked to lateralized brain function. The presence of the behavioral manifestations of these biases are associated with increased task success. Additionally, when an individual’s direction of bias aligns with the majority of the population, it is linked to social advantages. However, it remains unclear if behavioral biases in humans correlate with the same advantages. This large-scale study (N = 313–1661, analyses dependent) examines whether the strength and alignment of behavioral biases associate with cognitive and social benefits respectively in humans. To remain aligned with the animal literature, we evaluate motor-sensory biases linked to motor-sequencing and emotion detection to assess lateralization. Results reveal that moderate hand lateralization is positively associated with task success and task success is, in turn, associated with language fluency, possibly representing a cascade effect. Additionally, like other vertebrates, the majority of our human sample possess a ‘standard’ laterality profile (right hand bias, left visual bias). A ‘reversed’ profile is rare by comparison, and associates higher self-reported social difficulties and increased rate of autism and/or attention deficit hyperactivity disorder. We highlight the importance of employing a comparative theoretical framing to illuminate how and why different laterization profiles associate with diverging social and cognitive phenotypes.

An Automated Framework for Human Emotion Detection From Multichannel EEG Signals

An Automated Framework for Human Emotion Detection From Multichannel EEG Signals

Brain-Inspired Driver Emotion Detection for Intelligent Cockpits Based on Real Driving Data

Brain-Inspired Driver Emotion Detection for Intelligent Cockpits Based on Real Driving Data

Advancing Sentiment Analysis Through Emotionally-Agnostic Text Mining in Large Language Models (LLMS)

The conventional methodology for sentiment analysis within large language models (LLMs) has predominantly drawn upon human emotional frameworks, incorporating physiological cues that are inherently absent in text-only communication. This research proposes a paradigm shift towards an emotionallyagnostic approach to sentiment analysis in LLMs, which concentrates on purely textual expressions of sentiment, circumventing the confounding effects of human physiological responses. The aim is to refine sentiment analysis algorithms to discern and generate emotionally congruent responses strictly from text-based cues. This study presents a comprehensive framework for an emotionally-agnostic sentiment analysis model that systematically excludes physiological indicators whilst maintaining the analytical depth required for accurate emotion detection. A novel suite of metrics tailored to this approach is developed, facilitating a nuanced interpretation of sentiment within text data, which is paramount for enhancing user interaction across a spectrum of text mining applications, including recommendation systems and interactive AI characters. The research undertakes a critical comparative analysis, juxtaposing the newly proposed model with traditional sentiment analysis techniques, to evaluate efficacy enhancements and to substantiate its application potential. Further, the investigation delves into the short-term memory capabilities of LLMs, exploring the implications for character AI roleplaying interfaces and their ability to recall and respond to user input within a text-driven emotional framework. Findings indicate that this emotionally-agnostic sentiment analysis approach not only simplifies the sentiment assessment process within LLMs but also opens avenues for a more precise and contextually appropriate emotional response generation. This abstracted model, devoid of human physiological constraints, represents a significant advancement in text mining, fostering improved interactions and contributing to the evolution of LLMs as adept analytical tools in various domains where emotional intelligence is crucial.

Decoding Emotions: Machine Learning Approach to Speech Emotion Recognition

Speech Emotion Recognition (SER) stands at the forefront of human-computer interaction, offering profound implications for fields such as healthcare, education, and entertainment. This project report delves into the application of Machine Learning (ML) techniques for SER, aiming to discern the emotional content from speech signals. The report begins with an overview of the significance of SER in various domains, emphasizing the need for accurate and robust emotion detection systems. Following this,a detailed exploration of the methodologies employed in SER is presented, encompassing feature extraction techniques, classification algorithms, and model evaluation metrics. In the implementation phase, diverse ML algorithms such as Support Vector Machine (SVM), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN) are employed to classify emotional states from speech data. Feature sets including prosodic features, spectral features, and deep learning-based representations are extracted from the speech signals to capture nuanced emotional cues.

EMOTION BASED MUSIC RECOMMEDENTION SYSTEM

Abstract: Emotion-based music recommendation is a challenging task that has attracted significant research attention in recent years. This paper proposes a novel approach to emotion-based music recommendation using Support Vector Machines (SVMs). Our approach uses images as inputs instead of pronouns to capture the emotional state of the user more accurately. We collected a dataset of images associated with different emotional states and extracted features using Convolutional Neural Networks (CNNs). Then, we trained an SVM model to predict the emotional state of the user based on the input image. Finally, we used the output of the SVM model to recommend music tracks that are most suitable for the user’s emotional state. Our experiments on a large dataset show that our approach outperforms existing methods in terms of accuracy and user satisfaction. The proposed approach provides a promising direction for emotion-based music recommendation systems that can enhance the user experience by providing personalized music recommendations based on their emotional state. Keywords- Emotion Detection, Face Recognition, Music API.

On the Use of Pitch-Based Features for Detecting Simultaneous Fear Emotion and Deception Behavior From Speech

In the last years, endowing the machine with emotional and behavioral intelligence has been one of the most challenging issues in the human–computer interaction area. In this context, this paper investigates the detection of simultaneous fear emotion and deception behavior in speech. To do so, a set of 72 pitch-based features has been investigated first to recognize fear and deception separately. Then, different feature selection techniques have been used in order to select the most relevant ones that best discriminate between fear/nonfear and deception/nondeception classes. Next, a decision-level fusion approach based on the belief theory has been proposed to infer whether fear and deception are detected simultaneously. Simulation results carried on databases dealing with fear/nonfear emotions and deception/truth behaviors have shown classification results reaching 83.33% and 72.45% as accuracy rates for fear and deception classifiers, respectively. The proposed fusion approach has revealed a correspondence between fear emotion and deception behavior in speech modality.

Emotion-Aware In-Car Feedback: A Comparative Study

We investigate personalised feedback mechanisms to help drivers regulate their emotions, aiming to improve road safety. We systematically evaluate driver-preferred feedback modalities and their impact on emotional states. Using unobtrusive vision-based emotion detection and self-labeling, we captured the emotional states and feedback preferences of 21 participants in a simulated driving environment. Results show that in-car feedback systems effectively influence drivers’ emotional states, with participants reporting positive experiences and varying preferences based on their emotions. We also developed a machine learning classification system using facial marker data to demonstrate the feasibility of our approach for classifying emotional states. Our contributions include design guidelines for tailored feedback systems, a systematic analysis of user reactions across three feedback channels with variations, an emotion classification system, and a dataset with labeled face landmark annotations for future research.

Facial Emotion Recognition System Using CNN for Song Mapping

This project introduces a novel system that integrates Facial Emotion Recognition (FER) with music mapping to enhance human-computer interaction. Leveraging a custom Convolutional Neural Network (CNN) algorithm, developed using the FER2013 dataset, we classify emotions into four categories- happy, sad, neutral, and angry- employing Haar Cascade for precise face detection and grayscale conversion for optimal CNN input. Our custom CNN demonstrates superior performance, achieving a testing accuracy of 77.23%, notably surpassing established models like VGG16 and ResNet50, which achieved 55.87% and 62.76% respectively. This system swiftly identifies emotions and recommends songs from the "278k Emotion Labeled Spotify Songs" playlist, aiming to boost user satisfaction. Through nuanced comparisons with VGG16 and ResNet50, our approach underscores its inherent strengths, suggesting promising advancements in Facial Emotion Recognition (FER) and music recommendation systems. With a focus on precision in emotion detection, our system subtly elevates user experience, contributing meaningfully to ongoing research in the field.

Facial Emotion Detection System through Machine Learning approach

Facial Emotion Detection System through Machine Learning approach

Real Time Student Emotion Detection using Yolov5

The introduction of technology in the field of Education, especially in learner emotion detection plays an important role in the modern educational context. This research introduces the application of the YOLOV5 algorithm to detect learner emotions in real time during the classroom learning process. This research aims to see the performance of YOLOv5 in detecting student emotions by comparing YOLOv5 variants, namely YOLOv5m, YOLOv5n, YOLOv5l, YOLov5s, and YOLOv5x. The dataset used is a video recording of the learning process taken in classroom A3-02 in Building A, Informatics Engineering Study Program, Faculty, Engineering, University of Mataram, which is grouped into 3 classes, namely (Bored, Happy, and Neutral) with a total dataset of 451 images with dataset distribution divided into 87% training data, 8% validation data, and 4% testing data. Based on the tests conducted, YOLOv5m showed the best results with the highest accuracy reaching 89.60% on Mean Average Precision, with batch settings of 14 and epochs of 150. These results indicate that the YOLOv5 algorithm is effective in detecting learner emotions with a satisfactory level of performance and makes a significant contribution to learner emotion detection, underscoring the potential of this technology in enhancing interaction and learning in educational environments.

Integration of machine learning bi-modal engagement emotion detection model to self-reporting for educational satisfaction measurement

Integration of machine learning bi-modal engagement emotion detection model to self-reporting for educational satisfaction measurement

Enhanced Emotion Detection Model Using Dusky Canidae Optimization-Based Deep CNN Classifier

Emotion detection involves distinguishing the complex nature of emotions, cross-cultural variations, and ethical considerations. The proposed model addresses cross-cultural issues through diverse training and ensures ethical data usage through advanced machine-learning techniques to enhance accuracy. The research aims to develop a Dusky Canidae Optimization-based Deep Convolutional Neural Network model (DCO opt deep CNN) for emotion recognition in audio-visual data, benefiting various advancements. During preprocessing, audio and video are separated to enhance sound quality and eliminate data redundancy while retaining vital information. Features are then extracted and employed for training and testing using the automation and effectiveness of Deep CNNs. The concatenated features are optimized using a hybrid approach that combines Dusky Hawk and Canidae Wolf Optimization (CWO), offering simplicity and interpretability with optimal parameter settings. The proposed DCO model effectively classifies emotions within the data. Evaluating the database, the DCO model exhibits a performance enhancement of 1.28%, 2.91%, and 1.28% in accuracy, sensitivity, and specificity with respect to the existing learner memorizing optimization-based deep NN model.